Data Visualization (STAT 302)
The midterm attempts to bring together everything you have learned to date. You’ll be asked to replicate a series of graphics to demonstrate your skills and provide short descriptions/explanations regarding issues and concepts in ggplot2.
You are free to use any resource at your disposal such as notes, past labs, the internet, fellow students, instructor, TA, etc. However, do not simply copy and paste solutions. This is a chance for you to assess how much you have learned and determine if you are developing practical data visualization skills and knowledge.
The datasets used for this dataset are stephen_curry_shotdata_2014_15.txt, ga_election_data.csv, and ga_map.rda. May also need the nbahalfcourt.jpg image.
Below you can find a short description of the variables contained in stephen_curry_shotdata_2014_15.txt:
GAME_ID - Unique ID for each game during the seasonPLAYER_ID - Unique player IDPLAYER_NAME - Player’s nameTEAM_ID - Unique team IDTEAM_NAME - Team namePERIOD - Quarter or period of the gameMINUTES_REMAINING - Minutes remaining in quarter/periodSECONDS_REMAINING - Seconds remaining in quarter/periodEVENT_TYPE - Missed Shot or Made ShotSHOT_DISTANCE - Shot distance in feetLOC_X - X location of shot attempt according to tracking systemLOC_Y - Y location of shot attempt according to tracking systemThe ga_election_data.csv dataset contains the state of Georgia’s county level results for the 2020 US presidential election. Here is a short description of the variables it contains:
County - name of county in GeorgiaCandidate - name of candidate on the ballot,Election Day Votes - number of votes cast on election day for a candidate within a countyAbsentee by Mail Votes - number of votes cast absentee by mail, pre-election day, for a candidate within a countyAdvanced Voting Votes - number of votes cast in-person, pre-election day, for a candidate within a countyProvisional Votes - number of votes cast on election day for a candidate within a county needing voter eligibility verificationTotal Votes - total number of votes for a candidate within a countyWe have also included the map data for Georgia (ga_map.rda) which was retrieved using tigris::counties().
# load package(s)
library(tidyverse)
library(hexbin)
library(sf)
library(ggthemes)
library(patchwork)
# load steph curry data
shots <- read_delim(file = "data/stephen_curry_shotdata_2014_15.txt", delim = "|") %>%
janitor::clean_names()
# load ga election & map data
load("data/ga_map.rda")
ga_election_dat <- read.csv(file = "data/ga_election_data.csv") %>%
janitor::clean_names()Using the stephen_curry_shotdata_2014_15.txt dataset replicate, as close as possible, the graphics below (2 required, 1 optional/bonus). After replicating the graphics provide a summary of what the graphics indicate about Stephen Curry’s shot selection (i.e. distance from hoop) and shot make/miss rate and how they relate/compare across distance and game time (i.e. across quarters/periods).
Hints:
fig-width and fig-heightminimal theme and adjust from there# plot the plot
ggplot(shots, aes(as.factor(period), shot_distance)) +
geom_boxplot(varwidth = TRUE) +
facet_wrap(~event_type) +
scale_x_discrete(
name = "Quarter/Period",
labels = c("Q1", "Q2", "Q3", "Q4", "OT")
) +
scale_y_continuous(
name = NULL,
labels = scales::unit_format(unit = "ft"),
limits = c(0, NA),
breaks = seq(0, 40, 10),
minor_breaks = NULL
) +
labs(
title = "Stephen Curry",
subtitle = "2014-2015"
)+
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
strip.text = element_text(face = "bold", size = 12),
axis.title.x = element_text(face = "bold", size = 12),
panel.grid.major.x = element_blank()
)
Hints:
fig-width and fig-heightminimal theme and adjust from there"#5D3A9B" and "#E66100"annotate() is used to add labelsggplot(shots, aes(shot_distance, fill = event_type, color = event_type)) +
geom_density(alpha = 0.3) +
scale_fill_manual(values = c("#5D3A9B", "#E66100")) +
scale_color_manual(values = c("#5D3A9B", "#E66100"))+
scale_x_continuous(
name = NULL,
breaks = seq(0, 40, 10),
minor_breaks = NULL,
labels = scales::unit_format(unit = "ft")
) +
scale_y_continuous(
name = NULL,
breaks = NULL,
limits = c(0, 0.081),
expand = c(0, 0)
) +
annotate(
"text",
x = 3,
y = 0.04,
label = "Made Shots",
hjust = 0,
vjust = 0
) +
annotate(
"text",
x = 27,
y = 0.07,
label = "Missed Shots",
hjust = 0,
vjust = 0
) +
labs(
title = "Stephen Curry",
subtitle = "Shot Densities (2014-2015)"
)+
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
strip.text = element_text(face = "bold", size = 12),
axis.title.x = element_text(face = "bold", size = 12),
legend.position = "none",
panel.grid.major.x = element_blank()
)
Plot 3 is required for graduate students, but is optional for undergraduate students.
Hints:
fig-width and fig-height"grey", "red", "orange" "yellow" (don’t have to use "orange", you can get away with using only "red" and "yellow")15+ is the highest value, you need to set the limits in the appropriate scale while also also setting the na.value to the top color# importing image of NBA half court
court <- grid::rasterGrob(
jpeg::readJPEG(
source = "data/nbahalfcourt.jpg"
),
width = unit(1, "npc"),
height = unit(1, "npc")
)
# plot
basecourt <- ggplot() +
annotation_custom(
grob = court,
xmin = -250, xmax = 250,
ymin = -52, ymax = 418
) +
coord_fixed() +
xlim(250, -250) +
ylim(-52, 418)basecourt +
geom_hex(
data = shots,
aes(loc_x, loc_y),
bins = 20,
alpha = 0.7,
color = "grey"
) +
scale_fill_gradient(
name = "Shot\nAttempts",
low = "yellow",
high = "red",
breaks = c(0, 5, 10, 15),
labels = c(0, 5, 10, "15+"),
# set limit of shots to 0 to 15
limits = c(0, 15),
# any shots above 15 will be 'na' so set color to high value = red
na.value = "red"
) +
labs(
subtitle = "Shot Chart (2014-2015)",
title = "Stephen Curry"
) +
theme_void() +
theme(plot.title = element_text(face = "bold", size = 14))
Provide a summary of what the graphics above indicate about Stephen Curry’s shot selection (i.e. distance from hoop) and shot make/miss rate and how they relate/compare across distance and game time (i.e. across quarters/periods).
In plot 1: Unsurprisingly, there are the fewest shots during OT for both missed and made- which makes sense since not every game has an OT period. For all periods, the made shots for each quarter each have a longer IQR and lower median than the missed shots. And in plot 2:The density graph shows a similar trend from the box plots: made shots on average have a shorter distance than missed shots. These densities are both bi-modal, peaking around 5f and again around 25 ft. The made shots has the higher pick at 5ft (easier to make shots at a shorter distance) and the missed shots has a higher peak at 25 ft (harder to make shots at a longer distance.In plot 3: Stephen Curry made most of his shots in front of the basket, where his made-shots is a lot more than the missed-shots. And he also made most of his shots 25fts away from the baskets, where he made more missed-shots. These are the spots where most basketball player would do their shots.
Using the ga_election_data.csv dataset in conjunction with mapping data ga_map.rda replicate, as close as possible, the graphic below. Note the graphic is comprised of two plots displayed side-by-side. The plots both use the same shading scheme (i.e. scale limits and fill options).
Background Information: Holding the 2020 US Presidential election during the COVID-19 pandemic was a massive logistical undertaking. Voter engagement was extremely high which produced a historical high voting rate. Voting operations, headed by states, ran very monthly and encountered few COVID-19 related issues. The state of Georgia did a particularly good job at this by encouraging their residents to use early voting. About 75% of the vote in a typical county voted early! Ignoring county boundaries, about 4 in every 5 voters, 80%, in Georgia voted early.
While it is clear that early voting was the preferred option for Georgia voters, we want to investigate whether or not one candidate/party utilized early voting more than the other — we are focusing on the two major candidates. We created the graphic below to explore the relationship of voting mode and voter preference, which you are tasked with recreating.
After replicating the graphic provide a summary of how the two maps relate to one another. That is, what insight can we learn from the graphic.
Hints:
fig-width and fig-heightpatchwork packagepatchwork::plot_annotation() will be useful for adding graphic title and caption; you’ll also set the theme options for the graphic title and caption (think font size and face)ggthemes::theme_map() was used as the base theme for the plotsscale_*_gradient2() will be helpful"#5D3A9B" and "#1AFF1A"Add comments to the code below where indicated. The added comments should concisely describe what the following line(s) of code do in the data wrangling process
# data
ga_graph <- ga_election_dat %>%
# get the proportion of absentee and advanced votes out of total votes
mutate(
prop_pre_eday = (absentee_by_mail_votes + advanced_voting_votes) / total_votes
) %>%
# Get rid of all the -vote variables
select(-contains("_vote"))
# biden map data
biden_map_data <- ga_map %>%
# combine two tables together on the same country and the same candidates
left_join(
ga_graph %>%
filter(candidate == "Joseph R. Biden"),
by = c("name" = "county")
)
# trump map data
trump_map_data <- ga_map %>%
# combine two tables together on the same country and the same candidates
left_join(
ga_graph %>%
filter(candidate == "Donald J. Trump"),
by = c("name" = "county")
)
# biden plot
biden <- ggplot(data = biden_map_data) +
geom_sf(aes(fill = prop_pre_eday), show.legend = FALSE) +
scale_fill_gradient2(
high = "#5D3A9B",
low = "#1AFF1A",
midpoint = 0.75,
limits = c(0.5, 1),
# guide = "none"
) +
theme_map() +
ggtitle("Joseph R. Biden") +
labs(subtitle = "Democratic Nominee")+
theme(
plot.title = element_text(face = "bold", size = 14)
)
# trump plot
trump <- ggplot(data = trump_map_data) +
geom_sf(aes(fill = prop_pre_eday)) +
scale_fill_gradient2(
name = NULL,
high = "#5D3A9B",
low = "#1AFF1A",
midpoint = 0.75,
limits = c(0.5, 1),
# guide = "none",
breaks = c(0.5, 0.75, 1),
labels = scales::percent_format()
) +
theme_map() +
labs(
title = "Donald J. Trump",
subtitle = "Republican Nominee") +
theme(
plot.title = element_text(face = "bold", size = 14),
legend.justification = c(1, 1),
legend.position = c(1, 1)
)
# final plot
(biden + trump) +
plot_annotation(
title = "Percentage of votes from early voting",
caption = "Georgia:2021 US Presidents Election Results",
theme = theme(
plot.title = element_text(face = "bold", size = 24),
plot.caption = element_text(size = 12)
)
)
Provide a summary of how the two maps relate to one another. That is, what insight can we learn from the graphic.
These two maps are have the opposite information. If one area in graph A shows a high percentage, then the same area in the other graph tends to show a low percentage.
In 3-5 sentences, describe the core concept/idea and structure of the ggplot2 package.
ggplot2 separates data, the mapping of data to graphic elements, and the drawing of graphic elements that are not related to data, similar to the MVC framework in java. This allows ggplot2 users to clearly feel the real components of a data analysis graph, and to develop and adjust accordingly.
Describe each of the following:
ggplot():creates a new plot; this plot is blank without any other specificationsaes() : maps data to visual aestheticsgeoms:specifies the type of geometric object to visualizestats:statistical transformation on the data rather than visualizing the datascales: translation of data to visual propertiestheme():visual appearances that are not related to the dataExplain the difference between using this code geom_point(aes(color = VARIABLE)) as opposed to using geom_point(color = VARIABLE).
geom_point(aes(color = VARIABLE)): This maps data to color based on value(s) of VARIABLE, e.g. if there are three levels of VARIABLE, the data will be mapped to three different color based on the level. This option also automatically creates a legend to show which color is assigned to which level(s) of VARIABLE.
geom_point(color = VARIABLE): This makes all points one color called VARIABLE. No legend is created because there is no mapping from data to aesthetics.